Managing Your Cursor Library: How to Index Code Without Leaking Secrets

Quick answer: Manage your Cursor library by enabling Privacy Mode and using a .cursorignore file to exclude sensitive data. While indexing improves AI context, it can leak secrets if hardcoded keys are included. Use SimplyScan to detect exposed credentials before they are indexed into the LLM context window.

By Gabriel CA · Kraftwire Software

To manage your Cursor library without leaking secrets, you must enable Privacy Mode in settings and use a .cursorignore file to explicitly exclude sensitive files like .env, secrets.json, and large data dumps from the codebase index. While indexing provides the AI with the context needed for high-quality code generation, it can inadvertently upload hardcoded API keys or proprietary logic to LLM providers if not properly restricted.

The Cursor library and codebase indexing features are the engine behind the editor's ability to provide context-aware code completions and chat answers. By creating a local index of your files, Cursor allows the LLM to understand your entire project structure rather than just the file you are currently editing. However, this deep integration introduces a security boundary: if your codebase contains hardcoded secrets, sensitive customer data, or proprietary logic in comments, those items are fed into the model's context window during a query.

Managing your Cursor library effectively requires a balance between providing enough context for the AI to be helpful and restricting access to sensitive information that should never leave your local environment.

What Is The Cursor Library And How Does Indexing Work?

The Cursor library refers to the collection of indexed files and external documentation that the editor uses to ground its AI responses. When you open a folder in Cursor · which is forked directly from Visual Studio Code · it typically prompts you to index the codebase. This process creates a set of embeddings (mathematical representations of your code) stored locally.

This process is known as Retrieval-Augmented Generation (RAG). The library also includes any external documentation URLs you have added, allowing the AI to reference specific third-party API docs that might not have been in its original training data.

Is Codebase Indexing Safe For Proprietary Projects?

For most developers, the primary concern is whether their proprietary code is used to train future models. Cursor provides a Privacy Mode that, when enabled, ensures your code is never used for training by Cursor or its model providers like OpenAI and Anthropic.

However, even with Privacy Mode on, the code is still sent to the LLM providers to generate a response. This means if your code contains sensitive data, it is being transmitted over the wire. In SimplyScan's scans of 170 AI-built apps, 30% (51 apps) had at least one HIGH or CRITICAL severity issue. These often involve exposed API keys or environment variables security flaws. If these secrets exist in your files, they will likely be indexed and included in the AI context.

The Risk Of Context Leaks

• Hardcoded Keys: If you have an apiKey = \"sk-...\" in a utility file, the indexer sees it and may send it to the LLM provider.
• Sensitive Comments: Internal notes about infrastructure vulnerabilities provide a roadmap for potential exploits if the context is ever intercepted or logged.
• Data In Mock Files: Developers often include .json or .sql dumps for testing. If these contain real user data, that data is now part of your Cursor library.
• Prompt Injection: If an attacker can influence the files being indexed, they might attempt AI security prompt injection to leak data from the context.

How Do I Exclude Sensitive Files From The Cursor Index?

The most effective way to manage your Cursor library is to prevent sensitive files from being indexed in the first place. Cursor respects your .gitignore file by default, but you can provide more granular instructions using a .cursorignore file.

Using .cursorignore

Create a .cursorignore file in your root directory. This file works exactly like a .gitignore file. You should add:

• Large data files (*.csv, *.json, *.sql)
• Build artifacts (dist/, build/, .next/)
• Secret files that might not be in git (.env.local, secrets.yaml)
• Documentation that is irrelevant to coding tasks

By excluding these, you reduce the noise in your library, which often leads to more accurate AI responses while simultaneously improving your vibe-coding security checklist posture.

How To Manage External Documentation In Your Cursor Library?

Cursor allows you to add custom documentation by providing a URL. This is incredibly powerful for vibe-coding with new libraries like Lovable or Bolt, where the latest API changes might not be in the LLM's base training.

To manage this:

• Open the Cursor Settings (Cmd+Shift+J).
• Navigate to Features and then Docs.
• Add the base URL of the documentation you need.
• Cursor will crawl and index these pages, adding them to your project's library.

Be cautious about adding internal company wikis or private Notion pages as documentation. If the URL requires authentication, Cursor may not be able to crawl it properly, or it may prompt you to provide credentials that are then handled by the editor. For sensitive internal docs, it is better to keep them as local Markdown files that are indexed only if necessary.

What Are The Best Cursor Privacy Settings For Teams?

For professional teams, the default settings should be audited to prevent accidental data exposure. You can find these in the General tab of the settings.

• Privacy Mode: This should be toggled On for all commercial work. It prevents your code from being used for model training.
• Local Indexing Only: Ensure that the indexing process is happening on your machine. Cursor generally handles the embedding generation locally, but the resulting vectors are used to select context for the cloud-based LLM.
• Data Retention Policies: Review the privacy policy of the specific model provider you are using through Cursor. While Cursor may not train on your data, providers like OpenAI have specific retention periods for API-submitted data.

How Do I Clean Up A Corrupted Or Bloated Cursor Index?

Sometimes the Cursor library becomes confused because it has indexed old versions of files or too many irrelevant assets. This can lead to the AI suggesting deprecated patterns or failing to find the correct files. In SimplyScan's research, speed issues (medium) appeared in 121 apps (71%); keeping your development environment lean helps prevent the bloat that often migrates from the editor to the final production build.

Steps To Reset Your Index

• Go to Settings > Features > Codebase Indexing.
• Click on Delete Index or Rescan Codebase.
• Check your .cursorignore to ensure no large, unnecessary folders (like node_modules) are being accidentally included.
• Re-index the project.

A clean index not only improves security by ensuring deleted secrets are removed from the embeddings but also improves the performance of the AI's retrieval mechanism.

Can I Use Cursor With A Remote MCP Server?

The Model Context Protocol (MCP) is a newer way to extend the Cursor library's capabilities. Instead of just indexing files, an MCP server can provide the AI with real-time access to databases, Google Drive, or Slack.

While this makes the AI much more capable, it significantly expands the attack surface. If you connect an MCP server to your Cursor instance, you are essentially giving the LLM a live window into those services. Always use the mcp server security scanning approach to ensure that the tools you are connecting don't have permissions to perform destructive actions like DROP TABLE on your production data.

Why Should You Scan Your Codebase Before Indexing?

The biggest risk with the Cursor library is not the tool itself, but the content you feed it. If your codebase is insecure, the AI will learn those insecure patterns and replicate them across your project. Architecture issues (medium) appeared in 81 apps (48%) in SimplyScan's corpus, often because the AI was allowed to reference poorly structured or insecure legacy code.

Before you index a large project, it is a best practice to run a security audit checklist. This ensures you aren't accidentally indexing:

• Exposed API keys in your frontend code.
• Broken access control logic that the AI might think is correct.
• Missing security headers in your configuration files.
• CSRF protection gaps in your API routes.

Using a tool like SimplyScan can help you identify these issues in seconds. SimplyScan provides a free site health scanner for vibe-coded apps built with tools like Cursor, Lovable, and Windsurf. One free scan grades 8 dimensions · including security, speed, and AI visibility · in about 30 seconds with no signup required. It can detect exposed API keys, missing Supabase RLS, and env-var leaks that you definitely don't want sitting in your Cursor library index.

How To Verify Your App Is Secure After Using AI?

Once you have used Cursor to build your features, the final step is verification. AI-generated code is prone to subtle logic errors, especially regarding RLS policies or code injection prevention.

• Manual Review: Always read the code the AI generates. Never \"Accept All\" without a line-by-line check.
• Automated Scanning: Use the SimplyScan security scanner to check the deployed version of your app.
• Badge Verification: Once your app passes, you can use a verified security badge to show users that your AI-built project follows security best practices.

By maintaining a clean Cursor library and using Cursor settings for secure coding, you can leverage the speed of AI development without compromising the integrity of your codebase or your users' data. Always remember that the AI is only as secure as the context you provide it. Keeping your index free of secrets is the first line of defense in modern AI app security.

Advanced Indexing: Handling Large Repositories

When working with monorepos or massive codebases, Cursor's indexer can sometimes struggle. To maintain performance and security:

• Scope your indexing: Only index the subdirectories relevant to your current task.
• Use .cursorindexingignore: This is a specific file for the indexer that allows you to keep files in your editor but out of the AI's context.
• Monitor Resource Usage: Indexing can be CPU-intensive. If your system slows down, it is often a sign that your .cursorignore is missing a large directory like node_modules or venv.

By following these practices, you ensure that your Cursor library remains a tool for productivity rather than a source of data leakage. Regular audits of your application security checklist will help keep your AI-assisted workflow safe and efficient.